Abstract: Many believe that Cloud will reshape the entire ICT industry as a revolution. In this paper, we aim to pinpoint the challenges and issues of Cloud computing. We first discuss two related computing paradigms - Service-Oriented Computing and Grid computing, and their relationships with Cloud computing We then identify several challenges from the Cloud computing adoption perspective. Last, we will highlight the Cloud interoperability issue that deserves substantial further research and development.

Abstract: The Cloud computing emerges as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed dynamic computing environments for end-users. In this paper, we study the Cloud computing paradigm from various aspects, such as definitions, distinct features, and enabling technologies. This paper brings an introductional review on the Cloud computing and provides the state-of-the-art of Cloud computing technologies.

Abstract: With the development of parallel computing, distributed computing, grid computing, a new computing model appeared. The concept of computing comes from grid, public computing and SaaS. It is a new method that shares basic framework. The basic principles of cloud computing is to make the computing be assigned in a great number of distributed computers, rather then local computer or remoter server. The running of the enterprise’s data center is just like Internet. This makes the enterprise use the resource in the application that is needed, and access computer and storage system according to the requirement. This article introduces the background and principle of cloud computing, the character, style and actuality. This article also introduces the application field the merit of cloud computing, such as, it do not need user’s high level equipment, so it reduces the user’s cost. It provides secure and dependable data storage center, so user needn’t do the awful things such storing data and killing virus, this kind of task can be done by professionals. It can realize data share through different equipments. It analyses some questions and hidden troubles, and puts forward some solutions, and discusses the future of cloud computing. Cloud computing is a computing style that provide power referenced with IT as a service. Users can enjoy the service even he knows nothing about the technology of cloud computing and the professional knowledge in this field and the power to control it.

Abstract: Cloud computing emerges as one of the hottest topic in field of information technology. Cloud computing is based on several other computing research areas such as HPC, virtualization, utility computing and grid computing. In order to make clear the essential of cloud computing, we propose the characteristics of this area which make cloud computing being cloud computing and distinguish it from other research areas. The cloud computing has its own conceptional, technical, economic and user experience characteristics. The service oriented, loose coupling, strong fault tolerant, business model and ease use are main characteristics of cloud computing. Clear insights into cloud computing will help the development and adoption of this evolving technology both for academe and industry.

Abstract: Providing security in a distributed system requires more than user authentication with passwords or digital certificates and confidentiality in data transmission. The Grid and Cloud Computing Intrusion Detection System integrates knowledge and behavior analysis to detect intrusions.

Abstract: Infrastructure-as-a-Service (IaaS) cloud computing offers new possibilities to scientific communities. One of the most significant is the ability to elastically provision and relinquish new resources in response to changes in demand. In our work, we develop a model of an “elastic site” that efficiently adapts services provided within a site, such as batch schedulers, storage archives, or Web services to take advantage of elastically provisioned resources. We describe the system architecture along with the issues involved with elastic provisioning, such as security, privacy, and various logistical considerations. To avoid over- or under-provisioning the resources we propose three different policies to efficiently schedule resource deployment based on demand. We have implemented a resource manager, built on the Nimbus toolkit to dynamically and securely extend existing physical clusters into the cloud. Our elastic site manager interfaces directly with local resource managers, such as Torque. We have developed and evaluated policies for resource provisioning on a Nimbus-based cloud at the University of Chicago, another at Indiana University, and Amazon EC2. We demonstrate a dynamic and responsive elastic cluster, capable of responding effectively to a variety of job submission patterns. We also demonstrate that we can process 10 times faster by expanding our cluster up to 150 EC2 nodes.

Abstract: The HUBzero cyberinfrastructure lets scientific researchers work together online to develop simulation and modeling tools. Other researchers can then access the resulting tools using an ordinary Web browser and launch simulation runs on the national Grid infrastructure, without having to download or compile any code.

Abstract: Efficient task scheduling mechanism can meet users' requirements, and improve the resource utilization, thereby enhancing the overall performance of the cloud computing environment. But the task scheduling in grid computing is often about the static task requirements, and the resources utilization rate is also low. According to the new features of cloud computing, such as flexibility, virtualization and etc, this paper discusses a two levels task scheduling mechanism based on load balancing in cloud computing. This task scheduling mechanism can not only meet user's requirements, but also get high resource utilization, which was proved by the simulation results in the CloudSim toolkit.

Abstract: This paper presents a model for smart grid data management based on specific characteristics of cloud computing, such as distributed data management for real-time data gathering, parallel processing for real-time information retrieval, and ubiquitous access. The appliance of the cloud computing model meets the requirements of data and computing intensive smart grid applications. We gathered these requirements by analyzing the set of well-known smart grid use cases, most of which demand flexible collaboration across organizational boundaries of network operators and energy service providers as well as the active participation of the end user. Hence, preserving confidentiality and privacy, whilst processing the massive amounts of smart grid data, is of paramount importance in the design of the proposed Smart Grid Data Cloud.

Abstract: A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. Compared with grid environment, data transfer is a big overhead for cloud workflows due to the market-oriented business model in the cloud environments. In this paper, a Revised Discrete Particle Swarm Optimization (RDPSO) is proposed to schedule applications among cloud services that takes both data transmission cost and computation cost into account. Experiment is conducted with a set of workflow applications by varying their data communication costs and computation costs according to a cloud price model. Comparison is made on make span and cost optimization ratio and the cost savings with RDPSO, the standard PSO and BRS (Best Resource Selection) algorithm. Experimental results show that the proposed RDPSO algorithm can achieve much more cost savings and better performance on make span and cost optimization.

Abstract: Cloud computing, a rapidly developing information technology, has aroused the concern of the whole world. Cloud computing is Internet-based computing, whereby shared resources, software and information, are provided to computers and devices on-demand, like the electricity grid [1]. Cloud computing is the product of the fusion of traditional computing technology and network technology like grid computing, distributed computing parallel computing and so on. It aims to construct a perfect system with powerful computing capability through a large number of relatively low-cost computing entity, and using the advanced business models like SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service) to distribute the powerful computing capacity to end users' hands. This article introduces the background and service model of cloud computing. This article also introduces the existing issues in cloud computing such as security, privacy, reliability and so on. Proposition of solution for these issues has been provided also.

Abstract: A primary driving force of the recent cloud computing paradigm is its inherent cost effectiveness. As in many basic utilities, such as electricity and water, consumers/clients in cloud computing environments are charged based on their service usage, hence the term ‘pay-per-use’. While this pricing model is very appealing for both service providers and consumers, fluctuating service request volume and conflicting objectives (e.g., profit vs. response time) between providers and consumers hinder its effective application to cloud computing environments. In this paper, we address the problem of service request scheduling in cloud computing systems. We consider a three-tier cloud structure, which consists of infrastructure vendors, service providers and consumers, the latter two parties are particular interest to us. Clearly, scheduling strategies in this scenario should satisfy the objectives of both parties. Our contributions include the development of a pricing model—using processor-sharing—for clouds, the application of this pricing model to composite services with dependency consideration (to the best of our knowledge, the work in this study is the first attempt), and the development of two sets of profit-driven scheduling algorithms.

Abstract: Based on the deep research on Infrastructure as a Service (IaaS) cloud systems of open-source, we propose an optimized scheduling algorithm to achieve the optimization or sub-optimization for cloud scheduling problems. In this paper, we investigate the possibility to allocate the Virtual Machines (VMs) in a flexible way to permit the maximum usage of physical resources. We use an Improved Genetic Algorithm (IGA) for the automated scheduling policy. The IGA uses the shortest genes and introduces the idea of Dividend Policy in Economics to select an optimal or suboptimal allocation for the VMs requests. The simulation experiments indicate that our dynamic scheduling policy performs much better than that of the Eucalyptus, Open Nebula, Nimbus IaaS cloud, etc. The tests illustrate that the speed of the IGA almost twice the traditional GA scheduling method in Grid environment and the utilization rate of resources always higher than the open-source IaaS cloud systems.

Abstract: Deploying state-of-the-art technologies is vital and inevitable in assistive healthcare to cope with emerging services such as remote monitoring, collaborative consultation, and electronic health record. Grid computing has succeeded somewhat in enabling the sharing of resources across organizations but has not been deployed widely due to its complex implementation and interface. Cloud computing overcomes this aspect by allowing simple and easy user access, coping with users’ dynamic and elastic demands, providing metered usage for its resources and hence is increasingly being adopted by individual users as well as enterprise users. The Cloud may just be the right technology for healthcare infrastructure. However, several serious issues concerning security, data protection and ownership, quality of services, and mobility need to be resolved before Cloud computing can be widely adopted.. This paper proposes Mobile Cloud for Assistive Healthcare (MoCAsH) as an infrastructure for assistive healthcare. Besides inheriting the advantages of Cloud computing, MoCAsH embraces important concepts of mobile sensing, active sensor records, and collaborative planning by deploying intelligent mobile agents, context-aware middleware, and collaborative protocol for efficient resource sharing and planning. MoCAsH addresses security and privacy issues by deploying selective and federated P2P Cloud to protect data, preserve data ownership and strengthen aspects of security. It also addresses various quality-of-service issues concerning critical responses and energy consumption.

Abstract: Accurate maps of the static environment are essential for many advanced driver-assistance systems. A new method for the fast computation of occupancy grid maps with laser range-finders and radar sensors is proposed. The approach utilizes the Graphics Processing Unit to overcome the limitations of classical occupancy grid computation in automotive environments. It is possible to generate highly accurate grid maps in just a few milliseconds without the loss of sensor precision. Moreover, in the case of a lower resolution radar sensor it is shown that it is suitable to apply super-resolution algorithms to achieve the accuracy of a higher resolution laser-scanner. Finally, a novel histogram based approach for road boundary detection with lidar and radar sensors is presented.

Abstract: The emergence of cloud computing has established a trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid by significantly increasing the load at locations where they are built. However, data centers and cloud computing also provide opportunities to help the grid with respect to robustness and load balancing. To gain insights into these opportunities, we formulate the service request routing problem in cloud computing jointly with the power flow analysis in smart grid and explain how these problems can be related. Simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that a grid-aware service request routing design in cloud computing can significantly help in load balancing in the electric grid and making the grid more reliable and more robust with respect to link breakage and load demand variations.

Abstract: Cloud computing is a new emerging computing paradigm that advocates supplying users everything as a service. Compared with grid computing, the focus of resource management problem is transformed to resource virtualization and allocation rather than job decomposition and scheduling. It is more urgent to find better solutions for cloud resource allocation than ever before. Although there have been some research efforts in grid computing, most of them aim at maximizing utility of system and lack of analysis for competition between different users. Some researches consider competition analysis, but they assume that common knowledge is certain and known for every user, which is difficult to be applied in a global distributed cloud environment. In this paper, we hereby propose a new resource pricing and allocation policy where users can predict the future resource price as well as satisfy budget and deadline constraints. Experimental results prove that resource price can gradually converge to an equilibrium state by dynamic games and that cloud users can receive Nash equilibrium allocation proportion without other competitors' bidding information.

Abstract: Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.

Abstract: Achieving high performance for distributed I-O on a wide-area network continues to be an elusive holy grail. Despite enhancements in network hardware as well as software stacks, achieving high-performance remains a challenge. In this paper, our worldwide team took a completely new and non-traditional approach to distributed I-O, called ParaMEDIC: Parallel Metadata Environment for Distributed I-O and Computing, by utilizing application-specific transformation of data to orders of magnitude smaller metadata before performing the actual I-O. Specifically, this paper details our experiences in deploying a large-scale system to facilitate the discovery of missing genes and constructing a genome similarity tree by encapsulating the mpiBLAST sequence-search algorithm into ParaMEDIC. The overall project involved nine computational sites spread across the U.S. and generated more than a petabyte of data that was ‘teleported’ to a large-scale facility in Tokyo for storage. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract: The workflow paradigm has become the standard to represent processes and their execution flows. With the evolution of e-Science, workflows are becoming larger and more computational demanding. Such e-Science necessities match with what computational Grids have to offer. Grids are shared distributed platforms which will eventually receive multiple requisitions to execute workflows. With this, there is a demand for a scheduler which deals with multiple workflows in the same set of resources, thus the development of multiple workflow scheduling algorithms is necessary. In this paper we describe four different initial strategies for scheduling multiple workflows on Grids and evaluate them in terms of schedule length and fairness. We present results for the initial schedule and for the makespan after the execution with external load. From the results we conclude that interleaving the workflows on the Grid leads to good average makespan and provides fairness when multiple workflows share the same set of resources.

Abstract: It is a great idea to make many normal computers together to get a super computer, and this computer can do a lot of things. This is the concept of cloud computing. Cloud computing is an emerging model of business computing. And it is becoming a development trend. This article compares cloud computing and grid computing. Internet has connected all the computers in the world. Grid computing has been put forward under this background. Its core concept is to complete computing based on compute grid, in it every computer will devote power. In recent years a new concept cloud computing has been put forward, it can connect millions of computers to a super cloud. This article also introduces the application field the merit of cloud computing, such as, it do not need user's high level equipment, so it reduces the user's cost. It provides secure and dependable data storage center, so user needn't do the awful things such storing data and killing virus, this kind of task can be done by professionals. It can realize data share through different equipments. The users need not know how the cloud runs. In this paper I describe the concept of cloud computing and grid computing and compare them.

Abstract: The uptake of distributed infrastructures by scientific applications has been limited by the availability of extensible, pervasive and simple-to-use abstractions which are required at multiple levels -- development, deployment and execution stages of scientific applications. The Pilot-Job abstraction has been shown to be an effective abstraction to address many requirements of scientific applications. Specifically, Pilot-Jobs support the decoupling of workload submission from resource assignment, this results in a flexible execution model, which in turn enables the distributed scale-out of applications on multiple and possibly heterogeneous resources. Most Pilot-Job implementations however, are tied to a specific infrastructure. In this paper, we describe the design and implementation of a SAGA-based Pilot-Job, which supports a wide range of application types, and is usable over a broad range of infrastructures, i.e., it is general-purpose and extensible, and as we will argue is also interoperable with Clouds. We discuss how the SAGA-based Pilot-Job is used for different application types and supports the concurrent usage across multiple heterogeneous distributed infrastructure, including concurrent usage across Clouds and traditional Grids/Clusters. Further, we show how Pilot-Jobs can help to support dynamic execution models and thus, introduce new opportunities for distributed applications. We also demonstrate for the first time that we are aware of, the use of multiple Pilot-Job implementations to solve the same problem, specifically, we use the SAGA-based Pilot-Job on high-end resources such as the TeraGrid and the native Condor Pilot-Job (Glide-in) on Condor resources. Importantly both are invoked via the same interface without changes at the development or deployment level, but only an execution (run-time) decision.

Abstract: UNICORE is a European Grid Technology with more than 10 years of history. Originating from the Supercomputing domain, the latest version UNICORE 6 has turned into a general-purpose Grid technology that follows established standards and offers a rich set of features to its users. The paper starts with an architectural insight into UNICORE 6, highlighting the workflow features, standards and the different clients. Next, the current state of advancement is presented by describing recent developments. The paper closes with an outlook on future planned developments.

Abstract: Techniques are described for managing distributed execution of programs, including by dynamically scaling a cluster of multiple computing nodes used to perform ongoing distributed execution of a program, such as to increase and/or decrease the quantity of computing nodes in the cluster at various times and for various reasons. An architecture may be used that facilitates the dynamic scaling of a cluster, including by having at least some of the computing nodes act as core nodes that each participate in a distributed storage system for the distributed program execution, and having one or more other computing nodes that act as auxiliary nodes that do not participate in the distributed storage system. If computing nodes are selected to be removed from the cluster during ongoing distributed execution of a program, one or more nodes of the auxiliary computing node type may be selected for the removal.